1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
20/3/2023 Comida 52314 Tami NA
26/3/2023 Comida 24970 Andrés caramagnola
28/3/2023 Comida 71805 Tami NA
29/3/2023 Electricidad 42447 Andrés PAC ENEL 01686518
30/3/2023 Netflix 8320 Tami NA
31/3/2023 Comida 13226 Tami NA
31/3/2023 Comida 100000 Andrés wild foods
31/3/2023 Enceres 15400 Tami Incoludido
9/4/2023 Gas 67300 Andrés el de la derecha
10/4/2023 Comida 61792 Tami NA
17/4/2023 Comida 41602 Tami NA
19/4/2023 VTR 21990 Andrés NA
19/4/2023 nacho 55000 Andrés NA
22/4/2023 Comida 19420 Tami NA
23/4/2023 Comida 50617 Tami NA
23/4/2023 Crunchyroll 49900 Tami NA
23/4/2023 Netflix 5940 Tami NA
28/4/2023 Electricidad 43471 Andrés NA
29/4/2023 Comida 17000 Andrés pizza y dulces y nueces y almendras
30/4/2023 Comida 84066 Tami NA
30/4/2023 Parafina 38640 Tami NA
7/5/2023 Comida 53654 Tami NA
11/5/2023 Cruz Verde 21650 Tami Diolasa + propoleos
14/5/2023 Comida 69636 Tami NA
21/5/2023 Comida 71007 Tami NA
25/5/2023 Parafina 19788 Tami NA
27/5/2023 Electricidad 49600 Andrés NA
27/5/2023 Comida 76142 Tami NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 6.4272e+08   2    6.6405 0.0014 ** 
## lag_depvar    8.4253e+10   1 1740.9785 <2e-16 ***
## Residuals     2.8020e+10 579                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff      lwr      upr    p adj
## 1-0  7228.838  1016.35 13441.33 0.017685
## 2-0 28360.421 22686.67 34034.17 0.000000
## 2-1 21131.583 17753.78 24509.39 0.000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
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## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   427 50594.68 15385.061
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1985.27418   4025.96889   -523.73985   2450.49615  -2941.29101    529.04712 
##            8            9           10           11           12           13 
##  -5640.70271  -1204.79784  -3984.89472   -454.60045  -4971.10941  -1662.79242 
##           14           15           16           17           18           19 
##   -952.77455    328.95374  -3279.94502   -426.25231  -2171.46447   6558.57806 
##           20           21           22           23           24           25 
##  -1527.26481  -1212.54638   1467.84966  -1181.67493    235.03950   1699.79562 
##           26           27           28           29           30           31 
##  -7084.81474    924.07105   8180.64184    459.30512     27.58937  -2361.12556 
##           32           33           34           35           36           37 
##   1599.77335   4605.56698   1185.89869   2452.83849  -1797.06379   4662.16715 
##           38           39           40           41           42           43 
##   4335.74786  -2221.87459  -2947.47067  -1097.69595 -10736.82031   7230.55793 
##           44           45           46           47           48           49 
##   2548.93154   1374.84728   8120.44789    747.39355   6586.58637   6803.56514 
##           50           51           52           53           54           55 
##  -5764.67871  -4726.88467  -5027.70008  -7930.01189   6082.25934  -4081.90907 
##           56           57           58           59           60           61 
##  -4922.64333   3802.56467    864.18441    -47.32971    128.98308  -5006.92536 
##           62           63           64           65           66           67 
##  18088.51830   3713.93501  -3560.40566   5979.16398   7426.20257  14754.19808 
##           68           69           70           71           72           73 
##   1880.37127 -13038.78722  -1231.26360   4701.67927  -4821.82702  -4364.33195 
##           74           75           76           77           78           79 
## -10487.73138   2414.37377  -5430.67678   1005.61737  -6910.42272    468.61966 
##           80           81           82           83           84           85 
##  -2419.38030  -2763.83928  -4008.34051   -626.87764   2233.79021   3705.89630 
##           86           87           88           89           90           91 
##    449.50188   -505.48064    175.71972   4285.06333  -1152.41238   1153.57501 
##           92           93           94           95           96           97 
##  -2055.11708  -1047.90457    168.60064    268.23444  -7487.89464   2345.10274 
##           98           99          100          101          102          103 
##  -8628.98017  -3013.35569  -4119.64508  -1829.73004  -1352.14521   3094.79241 
##          104          105          106          107          108          109 
##  -2398.44695   2531.48175  -1197.63696    929.76634   2557.48899  -3164.91816 
##          110          111          112          113          114          115 
##  -4750.28740   -901.17618   1854.44545  11662.06302  -1201.81301   2697.23390 
##          116          117          118          119          120          121 
##   4303.76971   3563.58828  -1025.97881  -4657.73106  -3699.94745   2319.59432 
##          122          123          124          125          126          127 
##  -1718.93611   1342.70290   8868.40196    907.87215    188.84136  -2469.12054 
##          128          129          130          131          132          133 
##   2686.32693   7095.64308   1091.50943  -8424.07280   1765.90742   4160.61395 
##          134          135          136          137          138          139 
##  -3117.63790  -1397.34372   -842.34319  -3874.47390   1165.65608   -503.45413 
##          140          141          142          143          144          145 
##  -2923.12483   1693.23089  -1892.55408  -7849.95075   1976.20927  -3522.82084 
##          146          147          148          149          150          151 
##   2044.60830   -295.35057    988.68052   -383.12432   1329.53369   1174.93718 
##          152          153          154          155          156          157 
##   3353.50364  -4844.70988  -1187.53648  -3253.77437   5922.49378   9751.62889 
##          158          159          160          161          162          163 
##  -3378.48585  -4735.81813   3629.26160    253.48945   2764.20717  -5820.15805 
##          164          165          166          167          168          169 
##  -6687.05442   4186.20352  17457.19998   3791.47315   -221.94801  -2279.89281 
##          170          171          172          173          174          175 
##   -958.30245   3725.93710    -76.51138  -7930.27381   2960.49561   4439.32141 
##          176          177          178          179          180          181 
##    762.04312   8886.65507  -9068.90543  -3351.18200 -10644.45956 -11197.00891 
##          182          183          184          185          186          187 
##   1225.57496   9305.45106  -1356.15978   5998.63421   6660.34730  13295.66030 
##          188          189          190          191          192          193 
##   8629.02197  -3837.92816   2648.51088  10549.88506  -1424.52027  -2253.74452 
##          194          195          196          197          198          199 
## -10118.04123  -6264.49109   1297.64072  -5160.40820  -9747.84217   5387.86607 
##          200          201          202          203          204          205 
##  -3024.95357  -1678.11131   -770.82102   6531.17948   9956.30623    699.91244 
##          206          207          208          209          210          211 
##   3042.36776   3224.01682   5918.66547  12988.29473  -5480.71487 -11134.42899 
##          212          213          214          215          216          217 
##  -5570.41726 -10520.40809  -5056.87935   1530.23517 -12987.66348  16358.35082 
##          218          219          220          221          222          223 
##   7873.63753   1632.08204  26796.08971  12755.92322   7601.98049  14301.56094 
##          224          225          226          227          228          229 
##  -3599.96557  -1478.87290   4005.75446    586.29138   2955.04267   9210.75819 
##          230          231          232          233          234          235 
##   6065.74774  -1659.65804  -1610.55819   9617.74699 -11284.31617  -7133.49775 
##          236          237          238          239          240          241 
##  -8435.20853 -10039.75138   3093.60894   1392.79418  -8243.31812  -8971.03396 
##          242          243          244          245          246          247 
##   9079.55173  -7722.87257   2498.73146 -10267.51494  -4062.29335   1407.85399 
##          248          249          250          251          252          253 
##   1008.51732 -12295.02888   3614.28549   2065.77323   4235.23463   2185.39867 
##          254          255          256          257          258          259 
##  -1096.38788  11199.50321  20993.87003   3396.86017  -4076.80302   4266.03572 
##          260          261          262          263          264          265 
##  -1532.46210   3878.00481  -4704.60094 -10779.78100  -4670.57271   -483.62283 
##          266          267          268          269          270          271 
##  -5148.40614   8798.25230  -4215.86042   4234.07981  -2042.13331   4485.24795 
##          272          273          274          275          276          277 
##    782.75484   7376.91676  -1310.17266  12113.31889  -4452.21234   1824.57397 
##          278          279          280          281          282          283 
##   -273.97219   7940.40037  -4942.60724  -2646.61282 -11192.15990  -2646.56195 
##          284          285          286          287          288          289 
##  18672.58615   7880.96738   2854.94819   -508.03286   1013.14819   6499.94723 
##          290          291          292          293          294          295 
##   7000.70690 -18638.24387 -11086.71826  -8107.76107   9658.05675   3113.65930 
##          296          297          298          299          300          301 
##  -1121.72839  27456.80189  10221.01457   5077.90070   9694.51018   3049.39281 
##          302          303          304          305          306          307 
##   -849.69276   8056.86010 -24121.81067  -3461.05507   -112.63958  -6903.03156 
##          308          309          310          311          312          313 
##  -3927.30830   2970.27323  -9135.76590  -3198.40879  -8154.23836   1581.76614 
##          314          315          316          317          318          319 
##  -3115.74699   2083.68139  -4030.59792  27490.52603   -602.38956   3401.22721 
##          320          321          322          323          324          325 
##  10943.72455   5729.53526  32527.16344   5351.07038 -20704.95266   1934.38531 
##          326          327          328          329          330          331 
##   1248.43167  -6332.52251  -1629.91449 -33171.65821    909.39282  -2251.48796 
##          332          333          334          335          336          337 
##    -32.49544  -3092.19393   4164.47900   -334.44140  -6843.46785  -3020.65304 
##          338          339          340          341          342          343 
##  -2095.08419  -7579.51382   3938.94543  -1262.34482  -1625.66226   -879.89116 
##          344          345          346          347          348          349 
##    294.31784    605.49349  -1489.16636  -9318.98131 -13106.04606   2384.27373 
##          350          351          352          353          354          355 
##  -4230.17952  -3567.73341  -5888.97066   1835.53836   1484.18707   2863.53867 
##          356          357          358          359          360          361 
##  -3644.77282   -402.59624    793.14565   7134.02630    416.17702     99.95398 
##          362          363          364          365          366          367 
##   2719.28713  -2609.04704   -744.35592  -8611.86838  -4512.90880  -6103.96518 
##          368          369          370          371          372          373 
##  -4848.13660  -7153.82528   5106.13588    482.63936   7237.24783  -7499.07620 
##          374          375          376          377          378          379 
##  -2141.95094  -3267.06736  -2348.54127 -12338.57762   1999.32700 -10523.65457 
##          380          381          382          383          384          385 
##   5789.58563   9451.42348   3265.69362  -2253.63118   1742.00830   6883.56801 
##          386          387          388          389          390          391 
##  11561.57175  -5634.96214  -5222.02996    -36.00793   8681.42136   1947.92900 
##          392          393          394          395          396          397 
##  11351.45186  -9733.51363   2885.24597    824.75279    670.98940   -548.29678 
##          398          399          400          401          402          403 
##   -462.53255 -14390.25985   8595.51557  -1079.84824  -1270.05101   7085.15032 
##          404          405          406          407          408          409 
##  -7812.60565  -1191.04297  -2422.14418  -5708.02336  -2751.29617  -3805.86135 
##          410          411          412          413          414          415 
##  -8642.88598   6238.02559   1765.22538  -7241.53307  -7570.53386  14334.75068 
##          416          417          418          419          420          421 
##   3958.93842   4636.69586  -7888.77920  -4614.73413  -2477.08862   2944.24686 
##          422          423          424          425          426          427 
## -13877.03364  -2679.62608  -8982.97849   3120.95085   7099.85993   6715.08617 
##          428          429          430          431          432          433 
##  -3836.19311  -3977.87332  -4585.15494  -1657.83860  -5578.87905  -6499.74334 
##          434          435          436          437          438          439 
##  -5829.04675  -1276.34031   -725.29507  -4847.23453   2706.21143   4974.90052 
##          440          441          442          443          444          445 
##  -4909.14051  -2018.65805   1715.19606  -3692.16925   2975.50359  -6430.00548 
##          446          447          448          449          450          451 
## -11973.17982  -4393.15230   9763.30091  -1886.22526   4899.64679  -5711.45126 
##          452          453          454          455          456          457 
##   -974.94751    532.75232   3179.08275 -12107.42559   3505.59120  -6552.40851 
##          458          459          460          461          462          463 
##   6660.35050   3168.95226   2671.21024  -3676.75255   2252.70730    157.46282 
##          464          465          466          467          468          469 
##   1957.20568   -353.91237   3515.30450  -2468.38513   5968.98836  -6766.21192 
##          470          471          472          473          474          475 
##  -2803.81962  -2048.44497  -4507.97362   3146.30245   7957.95579  -5838.13024 
##          476          477          478          479          480          481 
##   1649.48777  -6008.67833  -2686.22661   2167.65323 -12765.92598  -9617.87057 
##          482          483          484          485          486          487 
##  -1080.18199    149.46741   -824.42001  -1199.24457  -9438.99086  11224.53028 
##          488          489          490          491          492          493 
##   6398.19162   7599.44391  -5241.88482   5546.01723   9482.56585   6260.50796 
##          494          495          496          497          498          499 
## -13260.48421 -10390.24393  -3288.75772   -956.23776   -371.67888  -7469.39908 
##          500          501          502          503          504          505 
##    755.25235   4440.65327   5676.99826    843.78751    265.18101  -7054.68142 
##          506          507          508          509          510          511 
##    737.50916  -4876.41239   1995.11107  -1125.60809  -7987.55970   -448.27579 
##          512          513          514          515          516          517 
##  -2516.48144   -431.13887   1492.52255  -9327.35540  -7616.97592  24422.46734 
##          518          519          520          521          522          523 
##  10026.40827   6099.27728  -5110.09428   3002.30828  17224.02559  11711.31963 
##          524          525          526          527          528          529 
## -23897.88759  -4879.56956  -3566.41211   4731.30095   -185.06373 -10932.24102 
##          530          531          532          533          534          535 
##   4536.52702  14069.92133  -4782.74748   4551.21415   5740.25725  -1596.37298 
##          536          537          538          539          540          541 
##  -4356.37312  -6902.92539  -1945.37250   8476.49813    304.00054  -7965.14586 
##          542          543          544          545          546          547 
##   1973.19993   -434.75307    531.76618 -10862.52111 -10917.12360   2165.76505 
##          548          549          550          551          552          553 
##   7143.20898  -1154.31234   1000.28992  -7552.19443   8717.82566   1084.62506 
##          554          555          556          557          558          559 
## -11763.92196   9314.31463   8835.04896    295.16860   5044.76701  -3375.67334 
##          560          561          562          563          564          565 
##  14293.36542  21712.23869  -6213.98778  -9460.05133   6962.76629    424.47418 
##          566          567          568          569          570          571 
##   3646.59612  -7182.93161 -17135.33645   6797.16993   6593.05745   2079.46129 
##          572          573          574          575          576          577 
##   3280.20609   1958.58184  -1973.92040  14900.95823  -9432.85588  -6059.50105 
##          578          579          580          581          582          583 
##   8879.60982   3048.09604  -6352.04489   7683.73134  -3607.24940  -2594.79793 
##          584 
##  15876.42354 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17284.01 20113.03 24339.88 24059.65 26398.01 23747.67 24459.42 19721.94 
##       10       11       12       13       14       15       16       17 
## 19460.18 16819.89 17592.40 14342.65 14393.49 15053.90 16739.66 15070.40 
##       18       19       20       21       22       23       24       25 
## 16098.46 15475.99 22513.26 21603.12 21086.29 22964.25 22294.53 22942.92 
##       26       27       28       29       30       31       32       33 
## 24777.10 18744.21 20459.36 28246.69 28303.98 27978.98 25623.51 27017.00 
##       34       35       36       37       38       39       40       41 
## 30835.53 31181.73 32581.92 30108.40 34107.25 37294.87 34369.76 31200.98 
##       42       43       44       45       46       47       48       49 
## 30056.11 20695.73 28166.50 30587.44 31669.69 38464.18 37961.99 42594.43 
##       50       51       52       53       54       55       56       57 
## 46803.68 39548.17 34151.27 29205.73 22393.88 28643.77 25246.21 21567.44 
##       58       59       60       61       62       63       64       65 
## 25947.67 27199.19 27494.30 27903.50 23800.77 40286.21 42118.41 37394.69 
##       66       67       68       69       70       71       72       73 
## 41574.80 46459.09 57059.20 55085.64 40422.98 37944.75 40943.40 35279.90 
##       74       75       76       77       78       79       80       81 
## 30761.16 21523.91 24704.96 20656.67 22729.42 17657.52 19660.09 18891.55 
##       82       83       84       85       86       87       88       89 
## 17925.48 16006.73 17276.35 20861.39 25250.93 26234.48 26259.28 26872.08 
##       90       91       92       93       94       95       96       97 
## 30970.84 29808.85 30801.83 28878.62 28083.54 28449.34 28853.32 22471.75 
##       98       99      100      101      102      103      104      105 
## 25467.55 18542.50 17405.93 15459.16 15757.00 16430.06 20874.16 19963.52 
##      106      107      108      109      110      111      112      113 
## 23452.21 23243.52 24908.94 27767.35 25281.43 21747.60 22021.27 24650.65 
##      114      115      116      117      118      119      120      121 
## 35445.81 33650.19 35475.94 38455.13 40398.55 38101.73 32955.80 29320.55 
##      122      123      124      125      126      127      128      129 
## 31390.08 29681.01 30855.03 38406.27 38051.02 37118.55 34002.10 35771.93 
##      130      131      132      133      134      135      136      137 
## 41135.35 40579.22 31837.09 33093.81 36263.21 32696.77 31094.34 30185.19 
##      138      139      140      141      142      143      144      145 
## 26764.20 28169.60 27940.70 25641.77 27653.27 26286.81 19929.79 22940.96 
##      146      147      148      149      150      151      152      153 
## 20781.53 23739.64 24276.18 25856.41 26037.32 27680.92 28973.35 31986.14 
##      154      155      156      157      158      159      160      161 
## 27485.25 26752.92 24323.79 30180.23 41398.91 39739.82 37121.60 42109.80 
##      162      163      164      165      166      167      168      169 
## 43509.36 46903.44 42398.34 37735.51 43126.09 59324.10 61522.09 59946.32 
##      170      171      172      173      174      175      176      177 
## 56792.30 55201.78 57887.08 56917.42 49258.79 52064.25 55782.96 55818.92 
##      178      179      180      181      182      183      184      185 
## 62902.19 53465.18 50236.89 41104.29 32697.71 36183.55 46222.45 45681.94 
##      186      187      188      189      190      191      192      193 
## 51596.65 57304.91 68018.98 73268.07 67003.06 67195.26 74220.38 69924.46 
##      194      195      196      197      198      199      200      201 
## 65475.90 54788.49 48856.79 50271.98 45894.84 38113.71 44497.38 42736.11 
##      202      203      204      205      206      207      208      209 
## 42376.39 42851.68 49602.27 58434.66 58066.63 59780.41 61425.62 65192.56 
##      210      211      212      213      214      215      216      217 
## 74598.57 66732.00 54996.56 49639.84 40693.74 37670.91 40764.66 30848.65 
##      218      219      220      221      222      223      224      225 
## 47713.65 54987.63 55883.77 78503.65 85950.73 87941.15 95483.97 86492.73 
##      226      227      228      229      230      231      232      233 
## 80529.53 80114.14 76785.53 75952.38 80659.11 82014.66 76485.70 71729.25 
##      234      235      236      237      238      239      240      241 
## 77346.74 64079.93 56167.35 48169.47 39834.68 43999.78 46138.75 39631.32 
##      242      243      244      245      246      247      248      249 
## 33351.31 43568.02 37851.70 41762.23 34075.58 32789.72 36421.63 39227.46 
##      250      251      252      253      254      255      256      257 
## 30115.57 36015.66 39792.77 44954.32 47655.25 47151.07 57386.13 74771.43 
##      258      259      260      261      262      263      264      265 
## 74587.66 67941.11 69413.46 65658.42 67095.32 60892.92 50236.14 46288.91 
##      266      267      268      269      270      271      272      273 
## 46496.98 42628.60 51376.43 47673.35 51793.56 49922.18 53963.53 54257.65 
##      274      275      276      277      278      279      280      281 
## 60236.60 57885.97 67497.07 61460.71 61669.40 60029.03 65735.18 59505.76 
##      282      283      284      285      286      287      288      289 
## 56091.59 45710.70 44117.70 61239.75 66734.48 67141.32 64575.42 63668.62 
##      290      291      292      293      294      295      296      297 
## 67644.01 71529.24 52647.29 42812.62 36861.94 47117.34 50338.44 49458.06 
##      298      299      300      301      302      303      304      305 
## 73499.70 79407.10 80070.49 84653.46 82863.55 77925.57 81370.24 56429.48 
##      306      307      308      309      310      311      312      313 
## 52714.50 52396.32 46226.17 43453.44 47033.77 39633.55 38363.81 32960.09 
##      314      315      316      317      318      319      320      321 
## 36720.46 35907.03 39714.03 37711.33 63332.96 61187.92 62801.13 70748.18 
##      322      323      324      325      326      327      328      329 
## 73120.27 98439.22 96827.24 72811.76 71617.28 69985.09 61988.20 59128.80 
##      330      331      332      333      334      335      336      337 
## 29269.04 32933.06 33369.78 35674.91 35019.95 40750.16 41818.90 37096.80 
##      338      339      340      341      342      343      344      345 
## 36316.23 36442.09 31790.91 37751.63 38410.81 38667.61 39537.83 41312.36 
##      346      347      348      349      350      351      352      353 
## 43122.74 42875.98 35865.62 26493.58 31804.18 30672.45 30265.11 27896.75 
##      354      355      356      357      358      359      360      361 
## 32545.81 36276.18 40711.34 38911.88 40164.14 42288.97 49637.11 50184.19 
##      362      363      364      365      366      367      368      369 
## 50384.57 52832.05 50331.50 49779.58 42471.62 39686.25 35887.57 33680.40 
##      370      371      372      373      374      375      376      377 
## 29763.29 37004.79 39277.18 47112.50 41122.52 40573.21 39119.83 38655.58 
##      378      379      380      381      382      383      384      385 
## 29581.39 34150.23 27246.13 35413.15 45680.45 49223.20 47507.56 49486.57 
##      386      387      388      389      390      391      392      393 
## 55667.14 65092.25 58346.74 52850.15 52580.58 59913.21 60433.26 69046.80 
##      394      395      396      397      398      399      400      401 
## 58221.75 59778.68 59341.58 58828.73 57325.25 56094.69 42937.48 51468.56 
##      402      403      404      405      406      407      408      409 
## 50475.34 49448.14 55808.75 48398.61 47714.14 46051.45 41756.15 40594.29 
##      410      411      412      413      414      415      416      417 
## 38670.46 32802.12 40624.92 43532.68 38238.82 33358.25 48135.49 51955.88 
##      418      419      420      421      422      423      424      425 
## 55860.21 48377.16 44723.80 43408.18 46971.89 35464.48 35195.41 29490.62 
##      426      427      428      429      430      431      432      433 
## 35045.00 43319.77 50168.19 46954.16 44041.44 40986.12 40875.02 37375.17 
##      434      435      436      437      438      439      440      441 
## 33538.05 30789.63 32355.72 34193.38 32210.65 37045.96 43212.14 39985.09 
##      442      443      444      445      446      447      448      449 
## 39692.95 42680.31 40579.78 44544.01 39821.04 30910.15 29754.98 41039.94 
##      450      451      452      453      454      455      456      457 
## 40723.50 46338.88 42002.66 42350.10 43960.35 47655.00 37593.41 42411.98 
##      458      459      460      461      462      463      464      465 
## 37864.22 45385.33 48883.08 51487.04 48237.29 50563.25 50763.51 52499.48 
##      466      467      468      469      470      471      472      473 
## 52000.27 54925.39 52270.58 57289.78 50592.39 48218.44 46813.55 43459.27 
##      474      475      476      477      478      479      480      481 
## 47191.62 54607.70 49069.94 50762.39 45584.23 43973.49 46788.50 36269.73 
##      482      483      484      485      486      487      488      489 
## 29872.04 31729.53 34409.13 35889.67 36849.42 30530.47 42981.38 49599.41 
##      490      491      492      493      494      495      496      497 
## 56386.46 51131.41 55933.86 63519.21 67306.48 53649.82 44287.33 42324.81 
##      498      499      500      501      502      503      504      505 
## 42645.96 43432.11 37953.75 40337.49 45605.43 51251.07 51956.25 52066.11 
##      506      507      508      509      510      511      512      513 
## 45807.92 47139.41 43422.32 46160.32 45828.13 39583.70 40707.62 39888.00 
##      514      515      516      517      518      519      520      521 
## 40986.62 43609.93 36495.40 31804.68 55543.02 63652.01 67281.81 60702.83 
##      522      523      524      525      526      527      528      529 
## 62033.83 75533.39 82465.89 57574.86 52477.41 49192.70 53543.92 53053.38 
##      530      531      532      533      534      535      536      537 
## 43299.19 48259.36 60839.60 55395.21 58771.31 62733.80 59805.09 54867.35 
##      538      539      540      541      542      543      544      545 
## 48371.09 47035.50 54922.29 54674.29 47281.51 49491.04 49318.81 50008.24 
##      546      547      548      549      550      551      552      553 
## 40716.55 32604.09 36918.36 44983.46 44781.71 46476.77 40524.60 49480.37 
##      554      555      556      557      558      559      560      561 
## 50628.35 40472.40 49952.81 57765.69 57134.66 60709.53 56503.63 68189.48 
##      562      563      564      565      566      567      568      569 
## 84772.13 74926.05 63562.23 67953.38 66089.69 67268.79 58892.34 42983.12 
##      570      571      572      573      574      575      576      577 
## 49947.23 55814.82 56990.08 59052.42 59695.35 56840.04 69008.86 58449.79 
##      578      579      580      581      582      583      584 
## 52212.68 59765.90 61260.33 54398.27 60624.96 56229.23 53292.58 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8373
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    6.640495  0.5449058    3.360267
## t2* 1740.978542 23.6431965  221.123654
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value  Upper CI
## 1 interrupt_var    2.422221       6.779964   13.4006
## 2    lag_depvar 1426.500391    1750.714549 2151.7093

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon May 29 00:54:16 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon May 29 00:54:26 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon May 29 00:54:36 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon May 29 00:54:46 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon May 29 00:54:56 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon May 29 00:55:06 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon May 29 00:55:17 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon May 29 00:55:27 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon May 29 00:55:37 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon May 29 00:55:47 2023
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua 0.0000 5.410333 5.629750 6.359175
Comida 370.2575 310.278417 314.087500 343.358350
Comunicaciones 0.0000 0.000000 0.000000 0.000000
Electricidad 31.5895 47.072333 38.297667 32.318925
Enceres 33.2250 20.086417 17.443792 25.492375
Farmacia 4.9950 1.831667 7.913875 9.458850
Gas/Bencina 44.0600 44.325000 28.954333 26.956100
Diosi 12.1450 31.180667 41.934250 37.511450
donaciones/regalos 0.0000 0.000000 7.170083 6.867975
Electrodomésticos/ Mantención casa 0.0000 3.944000 30.269500 20.736700
VTR 10.9950 25.156667 22.121792 20.106700
Netflix 5.6450 7.151583 7.090167 7.292775
Otros 0.0000 3.151083 1.575542 0.945325
Total 512.9120 499.588167 522.488250 537.404700
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1986, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-06-09 00:04:58 sería de: 36.396 pesos// Percentil 95% más alto proyectado: 39.596,03

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 36066.76 36065.69
Lo.80 36071.90 36069.74
Point.Forecast 36395.64 37104.26
Hi.80 38191.54 41922.45
Hi.95 39177.83 44473.05


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.3254  1006.1984
## s.e.  0.1382    33.3749
## 
## sigma^2 = 27182:  log likelihood = -331.76
## AIC=669.53   AICc=670.04   BIC=675.32
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.2965   656.0032  11.4970
## s.e.  0.1413   367.8739  12.0162
## 
## sigma^2 = 27292:  log likelihood = -331.33
## AIC=670.66   AICc=671.53   BIC=678.39
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 767.4879 664.4556 699.4717
Lo.80 884.8408 782.7447 812.0492
Point.Forecast 1106.5258 1006.1981 1024.7130
Hi.80 1328.2108 1229.6515 1285.1505
Hi.95 1445.5638 1347.9405 1423.0177


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.11         scales_1.2.1        ggiraph_0.8.7      
##  [7] tidytext_0.4.1      DT_0.28             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.1       xts_0.13.1         
## [13] forecast_8.21       wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.1     tm_0.7-11           NLP_0.2-1          
## [19] tsibble_1.1.3       lubridate_1.9.2     forcats_1.0.0      
## [22] dplyr_1.1.2         purrr_1.0.1         tidyr_1.3.0        
## [25] tibble_3.2.1        ggplot2_3.4.2       tidyverse_2.0.0    
## [28] sjPlot_2.8.14       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.8.1      httr_1.4.6         
## [34] readxl_1.4.2        zoo_1.8-12          stringr_1.5.0      
## [37] stringi_1.7.12      data.table_1.14.8   reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.8          readr_2.1.4        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          backports_1.4.1     systemfonts_1.0.4  
##   [4] selectr_0.4-2       lazyeval_0.2.2      splines_4.1.2      
##   [7] crosstalk_1.2.0     digest_0.6.31       htmltools_0.5.5    
##  [10] fansi_1.0.4         ggfortify_0.4.16    magrittr_2.0.3     
##  [13] tzdb_0.4.0          modelr_0.1.11       vroom_1.6.3        
##  [16] timechange_0.2.0    anytime_0.3.9       tseries_0.10-54    
##  [19] colorspace_2.1-0    xfun_0.39           crayon_1.5.2       
##  [22] jsonlite_1.8.4      lme4_1.1-33         glue_1.6.2         
##  [25] r2d3_0.2.6          gtable_0.3.3        emmeans_1.8.6      
##  [28] sjstats_0.18.2      sjmisc_2.8.9        car_3.1-2          
##  [31] quantmod_0.4.22     abind_1.4-5         mvtnorm_1.1-3      
##  [34] DBI_1.1.3           ggeffects_1.2.2     Rcpp_1.0.10        
##  [37] viridisLite_0.4.2   xtable_1.8-4        performance_0.10.3 
##  [40] bit_4.0.5           htmlwidgets_1.6.2   timeSeries_4030.106
##  [43] gplots_3.1.3        ellipsis_0.3.2      spatial_7.3-14     
##  [46] pkgconfig_2.0.3     farver_2.1.1        nnet_7.3-16        
##  [49] sass_0.4.5          dbplyr_2.3.2        janitor_2.2.0      
##  [52] utf8_1.2.3          tidyselect_1.2.0    labeling_0.4.2     
##  [55] rlang_1.1.0         munsell_0.5.0       cellranger_1.1.0   
##  [58] tools_4.1.2         cachem_1.0.7        cli_3.6.1          
##  [61] generics_0.1.3      sjlabelled_1.2.0    broom_1.0.4        
##  [64] evaluate_0.20       fastmap_1.1.1       yaml_2.3.7         
##  [67] knitr_1.43          bit64_4.0.5         caTools_1.18.2     
##  [70] forge_0.2.0         nlme_3.1-153        slam_0.1-50        
##  [73] xml2_1.3.3          tokenizers_0.3.0    compiler_4.1.2     
##  [76] rstudioapi_0.14     curl_5.0.0          bslib_0.4.2        
##  [79] highr_0.10          fBasics_4022.94     Matrix_1.5-4.1     
##  [82] its.analysis_1.6.0  nloptr_2.0.3        urca_1.3-3         
##  [85] vctrs_0.6.1         pillar_1.9.0        lifecycle_1.0.3    
##  [88] lmtest_0.9-40       jquerylib_0.1.4     estimability_1.4.1 
##  [91] bitops_1.0-7        insight_0.19.2      R6_2.5.1           
##  [94] KernSmooth_2.23-20  janeaustenr_1.0.0   codetools_0.2-18   
##  [97] gtools_3.9.4        boot_1.3-28         MASS_7.3-54        
## [100] assertthat_0.2.1    rprojroot_2.0.3     withr_2.5.0        
## [103] fracdiff_1.5-2      bayestestR_0.13.1   parallel_4.1.2     
## [106] hms_1.1.3           quadprog_1.5-8      timeDate_4022.108  
## [109] minqa_1.2.5         snakecase_0.11.0    rmarkdown_2.21     
## [112] carData_3.0-5       TTR_0.24.3          base64enc_0.1-3
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))